For each item, computes Anderson and Gerbing's (1991) substantive-validity coefficient:
$$C_{sv} = (n_c - n_o) / N,$$
where \(n_c\) is the number of non-missing assignments to the intended construct, \(n_o\) is the largest number of assignments to any one non-target construct, and \(N\) is the number of non-missing assignments.
Missing assignments are excluded itemwise and reported in n_missing.
Usage
compute_csv(
assignments,
item_col = "item",
rater_col = "rater",
assigned_col = "assigned_construct",
target_col = "target_construct"
)Value
A data.frame with one row per item and columns item, target,
n_total, n, n_missing, n_target, competitor, n_other_max, and csv.
It prints as a formatted table in APA style; the values themselves are
unrounded, and as.data.frame() returns the plain data frame.
References
Anderson, J. C., & Gerbing, D. W. (1991). Predicting the performance of measures in a confirmatory factor analysis with a pretest assessment of their substantive validities. Journal of Applied Psychology, 76(5), 732–740. doi:10.1037/0021-9010.76.5.732
Examples
df <- data.frame(
item = rep(c("I1", "I2"), each = 4),
rater = rep(1:4, 2),
assigned_construct = c("A", "A", "A", "B", "B", "B", "B", "B"),
target_construct = rep("A", 8)
)
compute_csv(df)
#> <contentvalid_csv> Coefficient of substantive validity (Csv)
#> Anderson and Gerbing (1991).
#>
#> Item Target Judges Competitor Competitor judges Csv
#> I1 A 3/4 B 1/4 .50
#> I2 A 0/4 B 4/4 -1.00
#>
#> Csv is the target count minus the count for the most-chosen other construct,
#> divided by the number of judges.
#>
#> See as.data.frame(x) for the unrounded values.